Virtual clothing try-on

ABSTRACT

A messaging system performs virtual clothing try-on. A method of virtual clothing try-on may include accessing a target garment image and a person image of a person wearing a source garment and processing the person image to generate a source garment mask and a person mask. The method may further include processing the source garment mask, the person mask, the target garment image, and a target garment mask to generate a warping, the warping indicating a warping to apply to the target garment image. The method may further include processing the target garment to warp the target garment in accordance with the warping to generate a warped target garment image, processing the warped target garment image to blend with the person image to generate a person with a blended target garment image, and processing the person with blended target garment image to fill in holes to generate an output image.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to virtualclothing try-on using neural networks within messaging systems. Moreparticularly, but not by way of limitation, embodiments of the presentdisclosure relate to processing an image of a person with sourceclothing and an image of target clothing and generating an image of theperson with the target clothing using neural networks to process theimages.

BACKGROUND

Enabling a person to virtual try-on clothing is complex because theimages of the person and the target clothing may not match in size,shape, or lighting. Traditional computer graphic methods are verycomplex to implement and computationally demanding, which may make theapplications too expensive to develop and which may make theapplications too computationally demanding for mobile devices.Additionally, the traditional computer graphic methods have haddifficulty in achieving images that are sufficiently realistic to enablea person to evaluate whether or not they would be interested inpurchasing the clothing that they have virtually tried on.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some embodiments are illustratedby way of example, and not limitation, in the figures of theaccompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging system, inaccordance with some examples, that has both client-side and server-sidefunctionality.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a flowchart for an access-limiting process, in accordance withsome examples.

FIG. 6 illustrates the operation of a virtual clothing try-on module, inaccordance with some embodiments.

FIG. 7 illustrates the operation of the interface module, in accordancewith some embodiments.

FIG. 8 illustrates the operation of the interface module, in accordancewith some embodiments.

FIG. 9 illustrates the operation of the virtual clothing try-on module,in accordance with some embodiments.

FIG. 10 illustrates the operation of the parsing module, in accordancewith some embodiments.

FIG. 11 illustrates the operation of training the garment warp module904, in accordance with some embodiments.

FIG. 12 illustrates the training of the inpainting module, in accordancewith some embodiments.

FIG. 13 illustrates the operation of training the fill-in module, inaccordance with some embodiments.

FIG. 14 illustrates the operation of blending module, in accordance withsome embodiments.

FIG. 15 illustrates results of virtual clothing try-on module, inaccordance with some embodiments.

FIG. 16 illustrates the operation of the harmonization module, inaccordance with some embodiments.

FIG. 17 illustrates the operation of training the harmonization module,in accordance with some embodiments.

FIG. 18 illustrates the operation of the harmonization module 908, inaccordance with some embodiments.

FIG. 19 illustrates the operation of post processing, in accordance withsome embodiments.

FIG. 20 illustrates elements of a dataset, in accordance with someembodiments.

FIG. 21 illustrates the operation of warping, in accordance with someembodiments.

FIG. 22 illustrates the operation of training a neural network, inaccordance with some embodiments.

FIG. 23 illustrates a method of virtual try-on, in accordance with someembodiments.

FIG. 24 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 25 is a block diagram showing a software architecture within whichexamples may be implemented.

FIG. 26 is a diagrammatic representation of a processing environment, inaccordance with some examples.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Disclosed is a messaging system that includes virtual clothing try-on.The virtual clothing try-on system takes an image of clothing to try-onwhich may be downloaded from the internet such as from an online storeand processes it along with an image of a person so that the person mayevaluate how they would look in the clothing.

The virtual clothing try-on system utilizes several neural networks andmay process the images in several phases. The virtual clothing try-onsystem may first determine how to warp the target clothing or clothingto try-on so that the target clothing may be shaped and sized in asimilar manner as the source clothing in the image of the person. Thetarget clothing is then warped and blended with the image of the personover the source clothing. The target clothing may have a whitebackground so that the warped targeted clothing image merged with theimage of the person will create holes or white spaces.

The virtual clothing try-on system will then fill-in the holes or whitespaces. The virtual clothing try-on system will then harmonize thetarget clothing by using lighting from the source clothing so that thelighting of the target clothing more closely matching the lighting ofthe image of the person and looks more natural. The virtual clothingmodule will then determine a texture of the source clothing and overlaythe texture on the target clothing so that the target clothing has arealistic texture for the image of the person.

The neural networks used for the virtual clothing try-on system may betrained end to end with a dataset that comprises an image of the personwearing the target clothing and a separate image of the target clothingin a standard position such as a flat lay which is often used fordisplaying clothing in online clothing stores.

A technical problem is how to achieve a high enough quality image sothat a person can determine whether they are interested in purchasingclothing based on how they would look in the clothing. The virtualclothing try-on system achieves a high quality virtual try-on image byperforming image processing in a series of stages as described abovewith multiple neural networks that are trained end-to-end with a groundtruth.

A technical problem is how to train a neural network to fill-in whitespaces after the target clothing has been blended with the image of theperson. The virtual clothing try-on system performs training on aconvolution neural network with a ground truth dataset with the lossesdetermined between the output of the network and the ground truth inputof the person. And where the losses are further determined by thedifference between a holes mask and an output holes mask. The holes maskis determined by subtracting a mask of the target garment from a mask ofthe warped garment. The output holes mask is determined by subtracting amask of the target garment from a mask of the target garment in theoutput image. Additionally, the input image is blended with the holesmask to create the holes. Training a neural network to fill-in whitespaces creates more realistic output images where the portion of theimage that is generated to cover the white space blends with theremainder of the output image more naturally.

Some embodiments generate a warp transformation between a target garmentand a source garment extracted from the image of the person. The warptransformation is generated without explicitly determining acorrespondence between the target garment and the garment worn by thetarget person. The task of the warp transformation is to warp the targetgarment such that it will be the same or similar to the garmentextracted from the image of the person. The warp transformation isperformed with a convolution neural network where supervised learning isused with a ground truth of images.

Some embodiments are turned to particular articles of clothing. Forexample, in one embodiment, the neural network are trained with a groundtruth dataset of t-shirts.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104. Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 and a messaging serversystem 108 via a network 106 (e.g., the Internet).

A messaging client 104 is able to communicate and exchange data withanother messaging client 104 and with the messaging server system 108via the network 106. The data exchanged between messaging client 104,and between a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (UIs) of the messaging client104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, application servers 112. Theapplication servers 112 are communicatively coupled to a database server118, which facilitates access to a database 120 that stores dataassociated with messages processed by the application servers 112.Similarly, a web server 124 is coupled to the application servers 112and provides web-based interfaces to the application servers 112. Tothis end, the web server 124 processes incoming network requests overthe Hypertext Transfer Protocol (HTTP) and several other relatedprotocols.

The Application Program Interface (API) server 110 receives andtransmits message data (e.g., commands and message payloads) between theclient device 102 and the application servers 112. Specifically, theApplication Program Interface (API) server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 112. The Application Program Interface (API) server110 exposes various functions supported by the application servers 112,including account registration, login functionality, the sending ofmessages, via the application servers 112, from a particular messagingclient 104 to another messaging client 104, the sending of media files(e.g., images or video) from a messaging client 104 to a messagingserver 114, and for possible access by another messaging client 104, thesettings of a collection of media data (e.g., story), the retrieval of alist of friends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph), the location of friends within a social graph, and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 112 host a number of server applications andsubsystems, including for example a messaging server 114, an imageprocessing server 116, and a social network server 122. The messagingserver 114 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor and memory intensive processing ofdata may also be performed server-side by the messaging server 114, inview of the hardware requirements for such processing.

The application servers 112 also include an image processing server 116that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 114.

The social network server 122 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 114. To this end, the social network server 122maintains and accesses an entity graph 306 (as shown in FIG. 3 ) withinthe database 120. Examples of functions and services supported by thesocial network server 122 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following,” and also the identification of other entities andinterests of a particular user.

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 112. The messaging system 100 embodies a numberof subsystems, which are supported on the client-side by the messagingclient 104 and on the server-side by the application servers 112. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, a modification system 206, a mapsystem 208, a game system 210, and a neural hair rendering system 214.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 114. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 furthermore includes a curationinterface 212 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface212 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 206 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system206 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 206 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 206 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text or image that can be overlaid on top of aphotograph taken by the client device 102. In another example, the mediaoverlay includes an identification of a location overlay (e.g., Venicebeach), a name of a live event, or a name of a merchant overlay (e.g.,Beach Coffee House). In another example, the augmentation system 206uses the geolocation of the client device 102 to identify a mediaoverlay that includes the name of a merchant at the geolocation of theclient device 102. The media overlay may include other indiciaassociated with the merchant. The media overlays may be stored in thedatabase 120 and accessed through the database server 118.

In some examples, the augmentation system 206 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 206 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 206 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 206 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time.

The map system 208 provides various geographic location functions andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 208 enables thedisplay of user icons or avatars (e.g., stored in profile data 308) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 210 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games that can be launched by auser within the context of the messaging client 104, and played withother users of the messaging system 100. The messaging system 100further enables a particular user to invite other users to participatein the play of a specific game, by issuing invitations to such otherusers from the messaging client 104. The messaging client 104 alsosupports both the voice and text messaging (e.g., chats) within thecontext of gameplay, provides a leaderboard for the games, and alsosupports the provision of in-game rewards (e.g., coins and items).

The virtual clothing try-on system 214 provides various functionsrelated to training neural networks 2206 and processing input image 602and garment image 604 to generate generated image 608 of FIG. 6 . Thevirtual clothing try-on system 214 may provide a means for user devices102 to download trained neural networks 2206 for virtual clothing try-onas well as download user interfaces such as is described in conjunctionwith FIGS. 6-8 for using virtual clothing try-on. The virtual clothingtry-on system 214 may provide access to a database of reference garmentimages 2006 for virtual try-on that may be remotely retrieved by theuser device 102.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 120 of the messaging server system 108,according to certain examples. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage and included within the message data stored in the message table302 is described below with reference to FIG. 4 .

An entity table 304 stores entity data, and is linked (e.g.,referentially) to an entity graph 306 and profile data 308. Entities forwhich records are maintained within the entity table 304 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 306 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization)interested-based or activity-based, merely for example.

The profile data 308 stores multiple types of profile data about aparticular entity. The profile data 308 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 308 includes, for example, a user name,telephone number, address, settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100, andon map interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 308 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 120 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 314) and images (for which data is stored in an image table316).

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage creation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 316includes augmented reality content items (e.g., corresponding toapplying Lenses or augmented reality experiences). An augmented realitycontent item may be a real-time special effect and sound that may beadded to an image or a video.

As described above, augmentation data includes augmented reality contentitems, overlays, image transformations, AR images, and similar termsrefer to modifications that may be applied to image data (e.g., videosor images). This includes real-time modifications, which modify an imageas it is captured using device sensors (e.g., one or multiple cameras)of a client device 102 and then displayed on a screen of the clientdevice 102 with the modifications. This also includes modifications tostored content, such as video clips in a gallery that may be modified.For example, in a client device 102 with access to multiple augmentedreality content items, a user can use a single video clip with multipleaugmented reality content items to see how the different augmentedreality content items will modify the stored clip. For example, multipleaugmented reality content items that apply different pseudorandommovement models can be applied to the same content by selectingdifferent augmented reality content items for the content. Similarly,real-time video capture may be used with an illustrated modification toshow how video images currently being captured by sensors of a clientdevice 102 would modify the captured data. Such data may simply bedisplayed on the screen and not stored in memory, or the contentcaptured by the device sensors may be recorded and stored in memory withor without the modifications (or both). In some systems, a previewfeature can show how different augmented reality content items will lookwithin different windows in a display at the same time. This can, forexample, enable multiple windows with different pseudorandom animationsto be viewed on a display at the same time.

Data and various systems using augmented reality content items or othersuch transform systems to modify content using this data can thusinvolve detection of objects (e.g., faces, hands, bodies, cats, dogs,surfaces, objects, etc.), tracking of such objects as they leave, enter,and move around the field of view in video frames, and the modificationor transformation of such objects as they are tracked. In variousembodiments, different methods for achieving such transformations may beused. Some examples may involve generating a three-dimensional meshmodel of the object or objects, and using transformations and animatedtextures of the model within the video to achieve the transformation. Inother examples, tracking of points on an object may be used to place animage or texture (which may be two dimensional or three dimensional) atthe tracked position. In still further examples, neural network analysisof video frames may be used to place images, models, or textures incontent (e.g., images or frames of video). Augmented reality contentitems thus refer both to the images, models, and textures used to createtransformations in content, as well as to additional modeling andanalysis information needed to achieve such transformations with objectdetection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device, or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device, and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of object's elements characteristic points for each element of anobject are calculated (e.g., using an Active Shape Model (ASM) or otherknown methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshused in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh, and thenvarious areas based on the points are generated. The elements of theobject are then tracked by aligning the area for each element with aposition for each of the at least one element, and properties of theareas can be modified based on the request for modification, thustransforming the frames of the video stream. Depending on the specificrequest for modification properties of the mentioned areas can betransformed in different ways. Such modifications may involve changingcolor of areas; removing at least some part of areas from the frames ofthe video stream; including one or more new objects into areas which arebased on a request for modification: and modifying or distorting theelements of an area or object. In various embodiments, any combinationof such modifications or other similar modifications may be used. Forcertain models to be animated, some characteristic points can beselected as control points to be used in determining the entirestate-space of options for the model animation.

In some examples of a computer animation model to transform image datausing face detection, the face is detected on an image with use of aspecific face detection algorithm (e.g., Viola-Jones). Then, an ActiveShape Model (ASM) algorithm is applied to the face region of an image todetect facial feature reference points.

In other examples, other methods and algorithms suitable for facedetection can be used. For example, in some embodiments, features arelocated using a landmark, which represents a distinguishable pointpresent in most of the images under consideration. For facial landmarks,for example, the location of the left eye pupil may be used. If aninitial landmark is not identifiable (e.g., if a person has aneyepatch), secondary landmarks may be used. Such landmark identificationprocedures may be used for any such objects. In some examples, a set oflandmarks forms a shape. Shapes can be represented as vectors using thecoordinates of the points in the shape. One shape is aligned to anotherwith a similarity transform (allowing translation, scaling, androtation) that minimizes the average Euclidean distance between shapepoints. The mean shape is the mean of the aligned training shapes.

In some examples, a search for landmarks from the mean shape aligned tothe position and size of the face determined by a global face detectoris started. Such a search then repeats the steps of suggesting atentative shape by adjusting the locations of shape points by templatematching of the image texture around each point and then conforming thetentative shape to a global shape model until convergence occurs. Insome systems, individual template matches are unreliable, and the shapemodel pools the results of the weak template matches to form a strongeroverall classifier. The entire search is repeated at each level in animage pyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, and any othersuitable image or video manipulation implemented by a convolutionalneural network that has been configured to execute efficiently on theclient device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client application 104operating on the client device 102. The transformation system operatingwithin the messaging client 104 determines the presence of a face withinthe image or video stream and provides modification icons associatedwith a computer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user's face within the image or video stream as part ofthe modification operation. Once a modification icon is selected, thetransform system initiates a process to convert the image of the user toreflect the selected modification icon (e.g., generate a smiling face onthe user). A modified image or video stream may be presented in agraphical user interface displayed on the client device 102 as soon asthe image or video stream is captured, and a specified modification isselected. The transformation system may implement a complexconvolutional neural network on a portion of the image or video streamto generate and apply the selected modification. That is, the user maycapture the image or video stream and be presented with a modifiedresult in real-time or near real-time once a modification icon has beenselected. Further, the modification may be persistent while the videostream is being captured, and the selected modification icon remainstoggled. Machine taught neural networks may be used to enable suchmodifications.

The graphical user interface, presenting the modification performed bythe transform system, may supply the user with additional interactionoptions. Such options may be based on the interface used to initiate thecontent capture and selection of a particular computer animation model(e.g., initiation from a content creator user interface). In variousembodiments, a modification may be persistent after an initial selectionof a modification icon. The user may toggle the modification on or offby tapping or otherwise selecting the face being modified by thetransformation system and store it for later viewing or browse to otherareas of the imaging application. Where multiple faces are modified bythe transformation system, the user may toggle the modification on oroff globally by tapping or selecting a single face modified anddisplayed within a graphical user interface. In some embodiments,individual faces, among a group of multiple faces, may be individuallymodified, or such modifications may be individually toggled by tappingor selecting the individual face or a series of individual facesdisplayed within the graphical user interface.

A story table 312 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 304). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom varies locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 314 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 316 storesimage data associated with messages for which message data is stored inthe entity table 304. The entity table 304 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 316 and the video table 314.

The database 120 can also store, referring to FIGS. 20 and 22 , thedataset 2200 and the weights 2206 of neural network 2204.

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server114. The content of a particular message 400 is used to populate themessage table 302 stored within the database 120, accessible by themessaging server 114. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 112. A message 400 is shown to include thefollowing example components:

Message identifier 402: a unique identifier that identifies the message400. Message text payload 404: text, to be generated by a user via auser interface of the client device 102, and that is included in themessage 400.

Message image payload 406: image data, captured by a camera component ofa client device 102 or retrieved from a memory component of a clientdevice 102, and that is included in the message 400. Image data for asent or received message 400 may be stored in the image table 316.

Message video payload 408: video data, captured by a camera component orretrieved from a memory component of the client device 102, and that isincluded in the message 400. Video data for a sent or received message400 may be stored in the video table 314.

Message audio payload 410: audio data, captured by a microphone orretrieved from a memory component of the client device 102, and that isincluded in the message 400.

Message augmentation data 412: augmentation data (e.g., filters,stickers, or other annotations or enhancements) that representsaugmentations to be applied to message image payload 406, message videopayload 408, or message audio payload 410 of the message 400.Augmentation data for a sent or received message 400 may be stored inthe augmentation table 310.

Message duration parameter 414: parameter value indicating, in seconds,the amount of time for which content of the message (e.g., the messageimage payload 406, message video payload 408, message audio payload 410)is to be presented or made accessible to a user via the messaging client104.

Message geolocation parameter 416: geolocation data (e.g., latitudinaland longitudinal coordinates) associated with the content payload of themessage. Multiple message geolocation parameter 416 values may beincluded in the payload, each of these parameter values being associatedwith respect to content items included in the content (e.g., a specificimage into within the message image payload 406, or a specific video inthe message video payload 408).

Message story identifier 418: identifier values identifying one or morecontent collections (e.g., “stories” identified in the story table 312)with which a particular content item in the message image payload 406 ofthe message 400 is associated. For example, multiple images within themessage image payload 406 may each be associated with multiple contentcollections using identifier values.

Message tag 420: each message 400 may be tagged with multiple tags, eachof which is indicative of the subject matter of content included in themessage payload. For example, where a particular image included in themessage image payload 406 depicts an animal (e.g., a lion), a tag valuemay be included within the message tag 420 that is indicative of therelevant animal. Tag values may be generated manually, based on userinput, or may be automatically generated using, for example, imagerecognition.

Message sender identifier 422: an identifier (e.g., a messaging systemidentifier, email address, or device identifier) indicative of a user ofthe Client device 102 on which the message 400 was generated and fromwhich the message 400 was sent.

Message receiver identifier 424: an identifier (e.g., a messaging systemidentifier, email address, or device identifier) indicative of a user ofthe client device 102 to which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 316.Similarly, values within the message video payload 408 may point to datastored within a video table 314, values stored within the messageaugmentations 412 may point to data stored in an augmentation table 310,values stored within the message story identifier 418 may point to datastored in a story table 312, and values stored within the message senderidentifier 422 and the message receiver identifier 424 may point to userrecords stored within an entity table 304.

Although the described flowcharts can show operations as a sequentialprocess, many of the operations can be performed in parallel orconcurrently. In addition, the order of the operations may bere-arranged. A process is terminated when its operations are completed.A process may correspond to a method, a procedure, an algorithm, etc.The operations of methods may be performed in whole or in part, may beperformed in conjunction with some or all of the operations in othermethods, and may be performed by any number of different systems, suchas the systems described herein, or any portion thereof, such as aprocessor included in any of the systems.

Time-Based Access Limitation Architecture

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message group 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client 104. In one example,an ephemeral message 502 is viewable by a receiving user for up to amaximum of 10 seconds, depending on the amount of time that the sendinguser specifies using the message duration parameter 506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message group 504 (e.g., a collection of messages in apersonal story, or an event story). The ephemeral message group 504 hasan associated group duration parameter 508, a value of which determinesa time duration for which the ephemeral message group 504 is presentedand accessible to users of the messaging system 100. The group durationparameter 508, for example, may be the duration of a music concert,where the ephemeral message group 504 is a collection of contentpertaining to that concert. Alternatively, a user (either the owninguser or a curator user) may specify the value for the group durationparameter 508 when performing the setup and creation of the ephemeralmessage group 504.

Additionally, each ephemeral message 502 within the ephemeral messagegroup 504 has an associated group participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message group504. Accordingly, a particular ephemeral message group 504 may “expire”and become inaccessible within the context of the ephemeral messagegroup 504, prior to the ephemeral message group 504 itself expiring interms of the group duration parameter 508. The group duration parameter508, group participation parameter 510, and message receiver identifier424 each provide input to a group timer 514, which operationallydetermines, firstly, whether a particular ephemeral message 502 of theephemeral message group 504 will be displayed to a particular receivinguser and, if so, for how long. Note that the ephemeral message group 504is also aware of the identity of the particular receiving user as aresult of the message receiver identifier 424.

Accordingly, the group timer 514 operationally controls the overalllifespan of an associated ephemeral message group 504, as well as anindividual ephemeral message 502 included in the ephemeral message group504. In one example, each and every ephemeral message 502 within theephemeral message group 504 remains viewable and accessible for a timeperiod specified by the group duration parameter 508. In a furtherexample, a certain ephemeral message 502 may expire, within the contextof ephemeral message group 504, based on a group participation parameter510. Note that a message duration parameter 506 may still determine theduration of time for which a particular ephemeral message 502 isdisplayed to a receiving user, even within the context of the ephemeralmessage group 504. Accordingly, the message duration parameter 506determines the duration of time that a particular ephemeral message 502is displayed to a receiving user, regardless of whether the receivinguser is viewing that ephemeral message 502 inside or outside the contextof an ephemeral message group 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message group 504based on a determination that it has exceeded an associated groupparticipation parameter 510. For example, when a sending user hasestablished a group participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message group 504 after thespecified twenty-four hours. The ephemeral timer system 202 alsooperates to remove an ephemeral message group 504 when either the groupparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message group 504 has expired, or when theephemeral message group 504 itself has expired in terms of the groupduration parameter 508.

In certain use cases, a creator of a particular ephemeral message group504 may specify an indefinite group duration parameter 508. In thiscase, the expiration of the group participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message group504 will determine when the ephemeral message group 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage group 504, with a new group participation parameter 510,effectively extends the life of an ephemeral message group 504 to equalthe value of the group participation parameter 510.

Responsive to the ephemeral timer system 202 determining that anephemeral message group 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (and, for example, specifically the messaging client 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage group 504 to no longer be displayed within a user interface ofthe messaging client 104. Similarly, when the ephemeral timer system 202determines that the message duration parameter 506 for a particularephemeral message 502 has expired, the ephemeral timer system 202 causesthe messaging client 104 to no longer display an indicium (e.g., an iconor textual identification) associated with the ephemeral message 502.

Virtual Clothing Try-on

FIG. 6 illustrates the operation 600 of a virtual clothing try-on module606, in accordance with some embodiments. Virtual clothing try-on module606 takes an input image 602 and a clothing image 604 and processes theimages to generate processed image 608. The AR image 608 enables aperson in the input image 602 to virtually try on the clothing of theclothing image 604.

The person image 602 is an input image of a person that may be from acamera such as camera 704 of FIG. 7 . The images are composed of pixelsthat include intensity and color values, in accordance with someembodiments. The images may include channels or layers of information,where each channel includes information for pixels. The images have adimensionality of pixels indicating a width and a height.

The clothing image 604 is a front image of a t-shirt, in accordance withsome embodiments. In some embodiments, the input image 602 is a frontalimage of the person and clothing image 604 is an image of a t-shirt witha flat lay. In some embodiments, the clothing image 604 is an image froma shopping site of clothing that may be available for purchase. In someembodiments the input image 602 is an image on a mobile device orcomputing device that is a live image of a person while the person isshopping. The person of the input image 602 may evaluate clothing byviewing the AR image 608, which may be displayed on a screen of a mobiledevice or computing device. Interface module 610 is configured toprovide an interface for a user of the virtual clothing try-on module606 as disclosed herein.

FIG. 7 illustrates the operation 700 of the interface module 610, inaccordance with some embodiments. A user is using a client device 102with an input image 702 displayed on the screen 702. The interfacemodule 610 is displaying clothing image 706, clothing image 708, andclothing image 710, as options to try-on the clothing on the person inthe input image 702. In some embodiments the input image 702 is a liveimage from camera 704. In some embodiments the input image 702 is animage from the messaging system. In some embodiments the input image 702is a saved image. In some embodiments the input image 702 is an imagefrom the internet.

FIG. 8 illustrates the operation 800 of the interface module 610, inaccordance with some embodiments. The user has selected clothing image708 for a virtual clothing try-on for the person in the input image 702of FIG. 7 . The interface module 610 has called virtual clothing try-onmodule 606 with input image 702 and clothing image 708 as input, whichhas processed the images to output generated image 804. In someembodiments the interface module 610 and virtual clothing try-on module606 perform their operations on the client device 102.

FIG. 9 illustrates the operation 900 of the virtual clothing try-onmodule 606, in accordance with some embodiments. The virtual clothingtry-on module 606 includes parsing module 902, garment warp module 904,inpainting module 906, and harmonization module 908, in accordance withsome embodiments.

FIG. 10 illustrates the operation 1000 of the parsing module 902, inaccordance with some embodiments. The parsing module 602 processes inputimage 1004 to generate image segmentation 1006, clothing segment 1004,and person segment 1006.

The clothing mask 1008 and person mask 1010 is derived from the imagesegmentation 1006, in accordance with some embodiments. In someembodiments the parsing module 902 is a trained convolution neuralnetwork (CNN) with multiple hierarchical semantic graphs. For example, afirst hierarchical semantic graph may include nodes for body parts of ahuman such as upper arms with a connection to torso, and torso mayindicate a connection to head. The head node may have an inter-graphconnection to a second hierarchical semantic graph to nodes hair, face,and hat. The hat node of the second hierarchical semantic graph may havean inter-graph connection to a third hierarchical semantic graph tonodes cap and helmet. The connections between the nodes may be directed.The hierarchical semantic graphs may be devised by a person in buildingthe CNN. In some embodiments the hierarchical sematic graphs arerepeated with convolution layers between the hierarchical sematicgraphs.

In some embodiments a ground truth dataset is used to train the parsingmodule 602 to train the weights 902. The ground truth dataset includesthousands of images with pixel-wise annotations on semantic part labelscorresponding to nodes of the hierarchical semantic graphs. The imagesinclude people in various positions. The image segmentation 1006 maylabel each of the different segments with one or more labels. Asillustrated parsing module 902 labels the various segments of the imagesegmentation 1006 and the clothing that is a shirt is separated out andturned into a clothing mask 1008. The process of turning an imagesegment into a mask is to blacken all pixels that are not part of theclothing and to turn all pixels that are part of the clothing as white.The parsing module 902 may be trained using the ground truth datasetthat includes the input image 1004 and an image segmentation 1006. Thelosses may be determined based on bitwise losses between the imagesegmentation 1006 generated by the parsing module 902 and the groundtruth image segmentation 1006, in accordance with some embodiments. Theweights 902 may be adjusted using backpropagation and gradient descent,in accordance with some embodiments.

FIG. 11 illustrates the operation 1100 of training the garment warpmodule 904, in accordance with some embodiments. First featureextraction CNN 1110 processes the garment mask 1102 and garment image1104 and feeds the output features into feature matching CNN 1114.Second feature extraction CNN 1112 processes person mask 1106 andgarment mask from person 1108 and feeds the output features into featurematching 1114. Feature matching CNN 1114 processes the extractedfeatures from the first feature extraction CNN 1110 and the secondfeature extraction CNN 1112 and generates a correlation map 1116. CNN1118 processes the correlation map 1116 and generates thin-plate spline(TPS) transformation parameters (Θ). In some embodiments parsing module902 generates warp grid 1122 based on the TPS transformation parameters.In some embodiments the warp grid 1122 is generated from processing thecorrelation map 1116 or output from the feature matching 1114. Warp 1124processes the warp grid 1122 with the garment mask 1102 and garmentimage 1104 to generate the warped garment image 1126 and mask of warpedgarment 1132.

The losses for the output for the garment warp module 904 are determinedbased on the mask losses 1134 between the garment mask from person 1108and the mask of warped garment 1132 and based on the image losses 1130between the warped garment image 1126 and the image and mask image 1128.The image and mask image 1128 is a combination of the garment mask fromperson 1108 and the image of the person such as input image 1004. Thelosses are determined using pixel losses, in accordance with someembodiments.

Each of the first feature extraction CNN 1110, second feature extractionCNN 1112, feature matching CNN 1114, predict TPS CNN 1118, TPSparameters (parms) 1120, and warp 1124 may be a CNN that has weights.All of these may be trained in an end-to-end pipeline where the lossesare determined as disclosed herein and the learning method is backpropagation with gradient descent, in accordance with some embodiments.

The training set includes person mask 1106, garment mask from person1108, garment mask 1102, garment image 1104, and image and mask 1128.The garment image 1104 is of the same garment that the person of theperson mask 1106 is wearing but the garment of the garment image 1104 iswarped differently. The training set is used as a ground truth to trainthe garment warp module 904 from end to end. The loss between two warpgrids 1122 may be determined using Equation (1). Equation (1):L_(reg)(G_(x),G_(y))=Σ_(i=−1,1)Σ_(x)Σ_(y)|G_(x)(x+i,y)−G_(x)(x,y)|+Σ_(j=−1,1)Σ_(x)Σ_(y)G_(x)(x,y+j)−G_(x)(x,y),where G is the warp grid, and x and y are coordinates on the warp grid.

FIG. 12 illustrates the training 1200 of the inpainting module 906, inaccordance with some embodiments. The warped garment image 1126 isoutput 1202 or generated from the garment warp module 904 from FIG. 11 .The blending module 1206 takes the warped garment image 1126 and theperson with garment image 1204 and processes the inputs to generate theblended garment image 1208. The person with garment image 1204 may bethe person image with the garment image 1104.

The blended garment image 1208 includes hole 1 1210 and hole 2 1212,which are a result of the warped garment image 1126 being a differentsize than the garment that the person with the garment image 1204 iswearing. Hole 1 1210 and hole 2 1212 may be white because the warpedgarment image 1126 has a white background.

Fill-in module 1214 processes the blended garment image 1208 andgenerates output image 1218. The losses for training are determined by abitwise comparison of output image 1218 and ground truth image 1220, inaccordance with some embodiments. The ground truth image 1220 is of theperson from which the person mask 1106 and garment mask from person 1108are derived as described in FIG. 11 . In some embodiments fill-in module1214 is a generative CNN (GCNN) or generative adversarial CNN (GAN) thathas weights that are trained by a gradient descent based on the imagelosses 1216.

FIG. 13 illustrates the operation 1300 of training the fill-in module1214, in accordance with some embodiments. The mask of warped garment1132 from FIG. 11 and garment mask 1102 are processed by subtracting thegarment mask 1102 from the mask of warped garment 1132 to generate theholes mask 1302. Add white holes module 1306 processes ground truthimage 1220 from FIG. 12 and holes mask 1302 to generate input image1308. The fill-in module 1214 processes the input image 1308 to generateoutput image 1310, which may be the same or similar as output image1126, and output holes mask 1312.

The losses are determined based on holes losses 1314 between the outputhole mask 1312 and the holes mask 1302 and the image losses 1316 betweenthe output image 1310 and the ground truth image 1220. Output hole mask1312 is determined by a mask of garment from the output image 1310subtracted by the garment mask 1102. The fill-in module 1214 may be aGNN that has weights that are trained by a gradient descent based on thedetermined losses.

FIG. 14 illustrates the operation 1400 of blending module 1404, inaccordance with some embodiments. Preprocessing 1304 from FIG. 13processes the garment masked image 1402 and the holes mask 1302 togenerate input image 1308. Fill-in module 1214 from FIG. 12 processesinput image 1308 and generates output image 1310. The blending module1404 processes output image 1310 and output holes mask 1312 to generateblended output image 1314. Ground truth image 1316 is illustrated forcomparison.

FIG. 15 illustrates results of virtual clothing try-on module 606, inaccordance with some embodiments. In example 1 1506, input image 1502and clothing image 1510 are processed by virtual clothing try-on module606 to generate generated image 1504. In example 2 1508, input image1502 and clothing image 1510 are processed by virtual clothing try-onmodule 606 to generate generated image 1504.

FIG. 16 illustrates the operation of the harmonization module 908, inaccordance with some embodiments. The harmonization module 908 processesthe blended output image 1604 and the garment mask from person 1602 togenerate the output image 1606. The harmonization module 908 may modifythe original lighting 1612 on the portion of the output image 1606 thatcorresponds to the garment mask from person 1602. The blended outputimage 1604 may have the clothing image 604 from FIG. 9 incorporated intothe blended output image 1604 but the lighting may not be in accordancewith the image lighting 1610 in the rest of the output image 1606because the lighting of the clothing image 604 may be different than theimage lighting 610 of the input image 602 of FIG. 6 .

FIG. 17 illustrates the operation 1700 of training the harmonizationmodule 908, in accordance with some embodiments. The globaldiscriminator 1706 processes real image 1704 or fake image 1702 anddetermines whether the process image is real or fake. The globaldiscriminator 1706 is trained in conjunction with the harmonizationmodule 908 where together they are an adversarial convolution neuralnetwork. The harmonization module 908 processes mask 1710 and outputimage 1708 to generate modified output image 1712, which may have thelight adjusted in areas that correspond to the mask 1710.

Domain discriminator 1722 processes mask with image 1718 and complementmask with image 1720 to determine image and mask domain 1724, whichindicates a similarity between the mask area and the image without themask area of the image. Similarly, domain discriminator 1722 processesmask with image modified image 1714 and complement mask with modifiedimage 1716 to determine modified image and mask domain 1726, whichindicates a similarity between the mask area and the modified imagewithout the mask area of the modified image. The harmonization module908 may be trained based on minimizing the loss of image and mask domain1724 and modified image and mask domain 1726.

FIG. 18 illustrates the operation 1800 of the harmonization module 908,in accordance with some embodiments. The harmonization module 908processes input images 1802 to generate output images 1804. In example 11806 the output image 1804 is darkened 1810 in areas where a t-shirt hasbeen added to better agree with the lighting of the rest of the image.In example 2 1808, portions of the output image 1804 are darkened 1182to better agree with the lighting of the rest of the image and portionsof the output image 1804 are lightened 1814 to better agree with thelighting of the rest of the output image 1804 that is not part of thet-shirt that was added to the image.

FIG. 19 illustrates the operation 1900 of post processing, in accordancewith some embodiments. Overlay blending module 1914 processes generatedimage 1910 and displacement map 1904 to generate output image 1916.Overlay blending module 1914 may be part of harmonization module 908 ormay be performed before or after harmonization module 908. The outputimage 1916 includes texture 1920 that is not in the generated image1910, which may be the same or similar as generated image 608 and wouldbe the image in the pipeline leading to overlay blending module 1914.The input image 1902, which may be the same or similar as input image602 of FIG. 9 , includes the texture 1908 and is processed by overlayblending module 1914 to generate displacement map 1904. In someembodiments input image 1902 is processed in accordance with contrastlimited adaptive histogram equalization (AHE)(CLAHE) 1906, which is anadaptive histogram equalization in which contrast amplification islimited, which reduces noise and brings out the texture. The overlayblending module 1914 may be a CNN or ACN that is trained to performpost-processing to add texture to the generated image 1910 from theinput image 1902.

FIG. 20 illustrates elements of a dataset 2000, in accordance with someembodiments. The person image 2002 is of a person that is wearing thesame garment that is in the garment image 2006. The person image 2002has a height of 256 pixels and a width of 192 pixels, in accordance withsome embodiments. The number of pixels may be different. Each pixelincludes color information and there may be multiple channels or layersof color information. The segment image 2004 is the person image 2002segmented into different segments for the different garments the personis wearing and segmented to only include the person. The garment image2006 is an image of the garment that the person of the person image 2002is wearing but not in a same shape. The garment image 2006 illustratesthe garment in a flat lay position, in accordance with some embodiments.In some embodiments the garment image 2006 illustrates the garment in adifferent position. In some embodiments the garment image 2006 is of thegarment in a standard position that is used for online sales. The personimage 2002, segment image 2004, and garment image 2006 may be used for aground truth input and output for training the various neural networksdisclosed herein.

FIG. 21 illustrates the operation of warping, in accordance with someembodiments. A garment image 2102 is a target garment that is to be puton the image of a person such as garment 604 of FIG. 6 . The garmentimage from person 2104 is a current garment the person is wearing. Thegoal of warp 2108 is to warp garment image 2102 is try and make itwarped and sized in a same way as the garment image from person 2104 sothat the warped garment image 2106 will fit on the person of thegenerated image 608 in a natural way.

FIG. 22 illustrates the operation of training a neural network, inaccordance with some embodiments. The training set 2200 may be a groundtruth such as from the dataset disclosed in conjunction with FIG. 20 .The training module 2202 trains the neural network 2204 in accordancewith the gradient descent and backpropagation in accordance with thelosses disclosed in conjunction with descriptions of the neuralnetworks. The neural networks 2204 are the various neural networkdisclosed herein. The weights 2206 are the weights and parametersassociated with the neural network 2204. The training module 2202 trainsthe neural networks 2204 to modify the weights 2206 until the losses arebelow some threshold of error. The production module 2208 may then usethe neural network 2210 with the weights 2206 to perform the variousfunctions described herein such as perform functions of the virtualclothing try-on module 606.

FIG. 23 illustrates a method 2300 of virtual try-on, in accordance withsome embodiments. The method begins at operation 2302 with accessing atarget garment image and a person image of a person wearing a sourcegarment. For example, input image 602 of FIG. 6 is the person image andgarment image 604 is the target garment image.

The method 2300 continues at operation 2304 with processing the personimage to generate a source garment mask and a person mask. For example,parsing module 902 of FIG. 10 generates person mask 1010 and clothingmask 1008 is the source garment mask.

The method 2300 continues at operation 2306 with processing the sourcegarment mask, the person mask, the target garment image, and a targetgarment mask to generate a warping, the warping indicating a warping toapply to the target garment image. For example, referring to FIG. 11 ,the source garment mask is the garment mask from person 1108, the personmask is the person mask 1106, the target garment image is garment image1104, and the target garment mask is the garment mask 1102. The warpingI the warp grid 1122.

The method 2300 continues at operation 2308 with processing the targetgarment to warp the target garment in accordance with the warping togenerate a warped target garment image. For example, warp 1124 of FIG.11 warps the garment image 1104 to generate warped garment image 1126.

The method 2300 continues at operation 2310 with processing the warpedtarget garment image to blend with the person image to generate a personwith a blended target garment image. For example, referring to FIG. 12 ,warped garment image 1126 is blended by blending module 1206 with personwith garment image 1204 to generate blended garment image 1208.

The method 2300 continues at operation 2312 with processing the personwith blended target garment image to fill in holes to generate an outputimage, the holes being a difference between the warped target garmentimage and an image of the source garment. For example, referring to FIG.12 , the fill-in module 1214 processes the blended garment image 1208 tofill-in hole 1 1210 and hole 2 1212 to generate the output image 1218.

Method 2300 may include one or more additional operations. One or moreof the operations of method 2300 may be optional. One or more of theoperations of method 2300 may be performed in a different order.

Machine Architecture

FIG. 24 is a diagrammatic representation of the machine 2400 withinwhich instructions 2408 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 2400to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 2408 may cause the machine 2400to execute any one or more of the methods described herein. Theinstructions 2408 transform the general, non-programmed machine 2400into a particular machine 2400 programmed to carry out the described andillustrated functions in the manner described. The machine 2400 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 2400 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 2400 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 2408, sequentially or otherwise,that specify actions to be taken by the machine 2400. Further, whileonly a single machine 2400 is illustrated, the term “machine” shall alsobe taken to include a collection of machines that individually orjointly execute the instructions 2408 to perform any one or more of themethodologies discussed herein. The machine 2400, for example, maycomprise the client device 102 or any one of a number of server devicesforming part of the messaging server system 108. In some examples, themachine 2400 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 2400 may include processors 2402, memory 2404, andinput/output I/O components 2438, which may be configured to communicatewith each other via a bus 2440. The processors 2402 may be termedcomputer processors, in accordance with some embodiments. In an example,the processors 2402 (e.g., a Central Processing Unit (CPU), a ReducedInstruction Set Computing (RISC) Processor, a Complex Instruction SetComputing (CISC) Processor, a Graphics Processing Unit (GPU), a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, aprocessor 2406 and a processor 2410 that execute the instructions 2408.The term “processor” is intended to include multi-core processors thatmay comprise two or more independent processors (sometimes referred toas “cores”) that may execute instructions contemporaneously. AlthoughFIG. 24 shows multiple processors 2402, the machine 2400 may include asingle processor with a single-core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiples cores, or any combinationthereof.

The memory 2404 includes a main memory 2412, a static memory 2414, and astorage unit 2416, both accessible to the processors 2402 via the bus2440. The main memory 2404, the static memory 2414, and storage unit2416 store the instructions 2408 embodying any one or more of themethodologies or functions described herein. The instructions 2408 mayalso reside, completely or partially, within the main memory 2412,within the static memory 2414, within machine-readable medium 2418within the storage unit 2416, within at least one of the processors 2402(e.g., within the Processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 2400.

The I/O components 2438 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 2438 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 2438 mayinclude many other components that are not shown in FIG. 24 . In variousexamples, the I/O components 2438 may include user output components2424 and user input components 2426. The user output components 2424 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 2426 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 2438 may include biometriccomponents 2428, motion components 2430, environmental components 2432,or position components 2434, among a wide array of other components. Forexample, the biometric components 2428 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 2430 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 2432 include, for example, one or cameras(with still image/photograph and video capabilities), illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetect ion sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera and a depth sensor, for example.

The position components 2434 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 2438 further include communication components 2436operable to couple the machine 2400 to a network 2420 or devices 2422via respective coupling or connections. For example, the communicationcomponents 2436 may include a network interface Component or anothersuitable device to interface with the network 2420. In further examples,the communication components 2436 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 2422 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 2436 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 2436 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF425, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components2436, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 2412, static memory 2414, andmemory of the processors 2402) and storage unit 2416 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 2408), when executedby processors 2402, cause various operations to implement the disclosedexamples.

The instructions 2408 may be transmitted or received over the network2420, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components2436) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 2408may be transmitted or received using a transmission medium via acoupling (e.g., a peer-to-peer coupling) to the devices 2422.

Software Architecture

FIG. 25 is a block diagram 2500 illustrating a software architecture2504, which can be installed on any one or more of the devices describedherein. The software architecture 2504 is supported by hardware such asa machine 2502 that includes processors 2520, memory 2526, and I/Ocomponents 2538. In this example, the software architecture 2504 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 2504 includes layerssuch as an operating system 2512, libraries 2510, frameworks 2508, andapplications 2506. Operationally, the applications 2506 invoke API calls2550 through the software stack and receive messages 2552 in response tothe API calls 2550.

The operating system 2512 manages hardware resources and provides commonservices. The operating system 2512 includes, for example, a kernel2514, services 2516, and drivers 2522. The kernel 2514 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 2514 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 2516 canprovide other common services for the other software layers. The drivers2522 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 2522 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 2510 provide a common low-level infrastructure used by theapplications 2506. The libraries 2510 can include system libraries 2518(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 2510 can include APIlibraries 2524 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 2510 can also include a widevariety of other libraries 2528 to provide many other APIs to theapplications 2506.

The frameworks 2508 provide a common high-level infrastructure that isused by the applications 2506. For example, the frameworks 2508 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 2508 canprovide a broad spectrum of other APIs that can be used by theapplications 2506, some of which may be specific to a particularoperating system or platform.

In an example, the applications 2506 may include a home application2536, a contacts application 2530, a browser application 2532, a bookreader application 2534, a virtual clothing try-on 2541 application, alocation application 2542, a media application 2544, a messagingapplication 2546, a game application 2548, and a broad assortment ofother applications such as a third-party application 2540. The virtualclothing try-on 2541 application may perform the operations as disclosedin conjunction with FIG. 9 and herein. The applications 2506 areprograms that execute functions defined in the programs. Variousprogramming languages can be employed to create one or more of theapplications 2506, structured in a variety of manners, such asobject-oriented programming languages (e.g., Objective-C, Java, or C++)or procedural programming languages (e.g., C or assembly language). In aspecific example, the third-party application 2540 (e.g., an applicationdeveloped using the ANDROID™ or IOS™ software development kit (SDK) byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™,ANDROID™, WINDOWS® Phone, or another mobile operating system. In thisexample, the third-party application 2540 can invoke the API calls 2550provided by the operating system 2512 to facilitate functionalitydescribed herein.

Processing Components

Turning now to FIG. 26 , there is shown a diagrammatic representation ofa processing environment 2600, which includes a processor 2602, aprocessor 2606, and a processor 2608 (e.g., a GPU, CPU or combinationthereof). The processor 2602 is shown to be coupled to a power source2604, and to include (either permanently configured or temporarilyinstantiated) modules, namely a training component 2610, a productioncomponent 2612, and a neural network component 2614. Referring to FIG.22 , the training component 2610 operationally trains neural network2204; the production component 2612 operationally performs theoperations for the neural network 2210 and user interface functions;and, the neural network component 2614 operationally assists inperforming operations for training and production of the neural network2204. As illustrated, the processor 2602 is communicatively coupled toboth the processor 2606 and the processor 2608.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a field-programmable gate array (FPGA) or an applicationspecific integrated circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In embodiments in which multiple hardwarecomponents are configured or instantiated at different times,communications between such hardware components may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware components have access. Forexample, one hardware component may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware component may then, at alater time, access the memory device to retrieve and process the storedoutput. Hardware components may also initiate communications with inputor output devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1602 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processors orprocessor-implemented components may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented components may be distributed across a number ofgeographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The plural of “computer-readablemedium” may be termed “computer-readable mediums”. U.S. Pat. No.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks: magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

What is claimed is:
 1. A method comprising: accessing a target garmentimage and a person image of a person wearing a source garment;processing the person image to generate a source garment mask and aperson mask; processing the source garment mask, the person mask, thetarget garment image, and a target garment mask to generate a warping,the warping indicating a warping to apply to the target garment image;processing the target garment to warp the target garment in accordancewith the warping to generate a warped target garment image; processingthe warped target garment image to blend with the person image togenerate a person with a blended target garment image; processing a maskof the warped target garment image to subtract the source garment maskto generate a subtraction mask; processing the person with the blendedtarget garment image to include holes from the subtraction mask; andprocessing the person with blended target garment image to fill in theholes to generate an output image.
 2. The method of claim 1 furthercomprising: processing the output image to generate an output garmentmask; processing the output garment mask to subtract the target garmentmask to generate an output mask; and processing the output image toblend the output image with the output mask and the person with theblended target garment to generate a modified output image.
 3. Themethod of claim 2 further comprising: processing the modified outputimage to adjust the lighting in accordance with lighting of the image ofthe source garment.
 4. The method of claim 1, wherein the processing thesource garment mask, the person mask, the target garment image, and thetarget garment mask to generate the warping is performed with aconvolution neural network.
 5. The method of claim 4 further comprising:training the convolution neural network using a ground truth with thetarget garment image being an image of the source garment, wherein thetraining is based on determining first losses between a mask of thewarped target garment image and a mask of the source garment and secondlosses between the warped target garment image and an image of thesource garment.
 6. The method of claim 1 wherein the target garmentimage is an image of the target garment in a flat lay position.
 7. Themethod of claim 1, wherein the person mask is a black and white imagewith all pixels that are not part of the image of the person set to ablack color and all pixels that are part of the image of the personassigned to the a white color.
 8. The method of claim 1, whereinprocessing the target garment to warp the target garment furthercomprises: processing the target garment to warp the target garment inaccordance with the warping to generate the warped target garment imageto warp the target garment image to match a source garment image.
 9. Themethod of claim 1 wherein the processing the person image is performedby a convolution neural network trained to segment images.
 10. Themethod of claim 1 wherein the processing the warped target garment imageto blend with the person image to generate a person with a blendedtarget garment image is performed with a convolution neural network. 11.The method of claim 1 wherein the processing the person with blendedtarget garment image to fill in holes is performed by a convolutionneural network.
 12. The method of claim 11 further comprising: trainingthe convolution neural network using a ground truth with the targetgarment image being an image of the source garment, wherein the trainingis based on determining first losses between the person image and theoutput image and second losses between a subtraction of a mask of thewarped target garment image and the mask of the target garment and asubtraction of a mask of an output garment of the output image and themask of the target garment.
 13. The method of claim 1 furthercomprising: processing the person image to generate a displacement map,the displacement map indicating texture of the source garment; andprocessing the output image to overlay and blend the displacement mapwith an image of the output garment of the output image.
 14. The methodof claim 13, wherein the processing the person image is performed by aconvolution neural network.
 15. The method of claim 13, wherein theprocessing the person image is performed in accordance with contrastlimited adaptive histogram equalization.
 16. The method of claim 1,wherein the target garment is a t-shirt.
 17. A system comprising: one ormore computer processors; and one or more computer-readable mediumsstoring instructions that, when executed by the one or more computerprocessors, cause the system to perform operations comprising: accessinga target garment image and a person image of a person wearing a sourcegarment; processing the person image to generate a source garment maskand a person mask; processing the source garment mask, the person mask,the target garment image, and a target garment mask to generate awarping, the warping indicating a warping to apply to the target garmentimage; processing the target garment to warp the target garment inaccordance with the warping to generate a warped target garment image;processing the warped target garment image to blend with the personimage to generate a person with a blended target garment image;processing a mask of the warped target garment image to subtract thesource garment mask to generate a subtraction mask; processing theperson with the blended target garment image to include holes from thesubtraction mask; and processing the person with blended target garmentimage to fill in the holes to generate an output image.
 18. Anon-transitory computer-readable storage medium including instructionsthat, when processed by a computer, configure the computer to performoperations comprising: accessing a target garment image and a personimage of a person wearing a source garment; processing the person imageto generate a source garment mask and a person mask; processing thesource garment mask, the person mask, the target garment image, and atarget garment mask to generate a warping, the warping indicating awarping to apply to the target garment image; processing the targetgarment to warp the target garment in accordance with the warping togenerate a warped target garment image; processing the warped targetgarment image to blend with the person image to generate a person with ablended target garment image; processing a mask of the warped targetgarment image to subtract the source garment mask to generate asubtraction mask; processing the person with the blended target garmentimage to include holes from the subtraction mask; and processing theperson with blended target garment image to fill in the holes togenerate an output image.